{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/visser/drive/PhD/Code/pain-measurement\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "if Path.cwd().stem == \"features\":\n",
    "    %cd ../..\n",
    "    %load_ext autoreload\n",
    "    %autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</div>\n",
       "<script type=\"application/javascript\">(function(root) {\n",
       "  var docs_json = {\"9371681f-8ffd-4e78-a4c2-377b90267861\":{\"version\":\"3.3.3\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"p1002\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"p1003\",\"attributes\":{\"plot_id\":\"p1002\",\"comm_id\":\"973b94c16665485d9721f6223bb9b2fe\",\"client_comm_id\":\"b3eb4540938a4c5f8d1f8ba59eafadaf\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
       "  var render_items = [{\"docid\":\"9371681f-8ffd-4e78-a4c2-377b90267861\",\"roots\":{\"p1002\":\"dc66710b-bcfa-4679-b66f-932764bb129e\"},\"root_ids\":[\"p1002\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ]
     },
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p1002"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import logging\n",
    "import os\n",
    "from dataclasses import dataclass\n",
    "from functools import reduce, wraps\n",
    "from pathlib import Path\n",
    "from typing import Dict, List\n",
    "\n",
    "import holoviews as hv\n",
    "import hvplot.polars\n",
    "import matplotlib.pyplot as plt\n",
    "import mne\n",
    "import neurokit2 as nk\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import panel as pn\n",
    "import plotly.express as px\n",
    "import polars as pl\n",
    "\n",
    "from src.data.config_data import DataConfigBase\n",
    "from src.data.config_data_raw import RAW_DICT, RAW_LIST, RawConfig\n",
    "from src.data.config_participant import PARTICIPANT_LIST, ParticipantConfig\n",
    "from src.data.make_dataset import load_dataset\n",
    "from src.features.quality_checks import check_sample_rate\n",
    "from src.features.transformations import (\n",
    "    add_timedelta_column,\n",
    "    interpolate,\n",
    "    map_participant_datasets,\n",
    "    map_trials,\n",
    "    merge_dfs,\n",
    "    scale_min_max,\n",
    "    scale_standard,\n",
    ")\n",
    "from src.log_config import configure_logging\n",
    "from src.visualization.plot_data import plot_data_panel, plot_trial_matplotlib\n",
    "\n",
    "configure_logging(\n",
    "    stream_level=logging.DEBUG, ignore_libs=[\"matplotlib\", \"Comm\", \"bokeh\", \"tornado\"]\n",
    ")\n",
    "\n",
    "pl.Config.set_tbl_rows(7)  # don't print too many rows in the book\n",
    "plt.rcParams[\"figure.figsize\"] = [15, 5]  # default is [6, 4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15:35:44 | \u001b[36mDEBUG   \u001b[0m| process_data | Dataset 'eeg' for participant 004_zoey3 loaded from data/raw/004_zoey3/004_zoey3_eeg.csv\n",
      "15:35:44 | \u001b[36mDEBUG   \u001b[0m| process_data | Dataset 'heat' for participant 004_zoey3 loaded from data/raw/004_zoey3/004_zoey3_heat.csv\n",
      "15:35:44 | \u001b[93mWARNING \u001b[0m| root | Working with unique timestamps.\n",
      "15:35:44 | \u001b[36mDEBUG   \u001b[0m| quality_check | Sample rate per trial: [499.98]\n",
      "15:35:44 | \u001b[92mINFO    \u001b[0m| quality_check | The mean sample rate is 499.98.\n",
      "15:35:44 | \u001b[92mINFO    \u001b[0m| quality_check | Checking sample rate for unique timestamps.\n",
      "15:35:44 | \u001b[36mDEBUG   \u001b[0m| quality_check | Sample rate per trial: [499.98]\n",
      "15:35:44 | \u001b[92mINFO    \u001b[0m| quality_check | The mean sample rate is 499.98.\n"
     ]
    }
   ],
   "source": [
    "participant_number = 2\n",
    "modality = \"eeg\"\n",
    "data_config = RAW_DICT[modality]\n",
    "sampling_rate = 500  # data_config.sampling_rate\n",
    "\n",
    "\n",
    "eeg_raw = load_dataset(PARTICIPANT_LIST[participant_number], RAW_DICT[modality]).dataset\n",
    "heat = load_dataset(PARTICIPANT_LIST[participant_number], RAW_DICT[\"heat\"]).dataset\n",
    "\n",
    "# eeg_raw = eeg_raw.unique('Timestamp').sort('Timestamp')\n",
    "logging.warning(\"Working with unique timestamps.\")\n",
    "\n",
    "check_sample_rate(eeg_raw)\n",
    "check_sample_rate(eeg_raw, unique_timestamp=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating RawArray with float64 data, n_channels=8, n_times=139999\n",
      "    Range : 0 ... 139998 =      0.000 ...   279.996 secs\n",
      "Ready.\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | bytecode dump:\n",
      ">          0\tNOP(arg=None, lineno=1119)\n",
      "           2\tRESUME(arg=0, lineno=1119)\n",
      "           4\tLOAD_GLOBAL(arg=1, lineno=1121)\n",
      "          16\tLOAD_ATTR(arg=1, lineno=1121)\n",
      "          26\tLOAD_FAST(arg=0, lineno=1121)\n",
      "          28\tLOAD_GLOBAL(arg=0, lineno=1121)\n",
      "          40\tLOAD_ATTR(arg=2, lineno=1121)\n",
      "          50\tPRECALL(arg=2, lineno=1121)\n",
      "          54\tCALL(arg=2, lineno=1121)\n",
      "          64\tSTORE_FAST(arg=2, lineno=1121)\n",
      "          66\tLOAD_GLOBAL(arg=7, lineno=1122)\n",
      "          78\tLOAD_FAST(arg=0, lineno=1122)\n",
      "          80\tPRECALL(arg=1, lineno=1122)\n",
      "          84\tCALL(arg=1, lineno=1122)\n",
      "          94\tGET_ITER(arg=None, lineno=1122)\n",
      ">         96\tFOR_ITER(arg=10, lineno=1122)\n",
      "          98\tSTORE_FAST(arg=3, lineno=1122)\n",
      "         100\tLOAD_FAST(arg=3, lineno=1123)\n",
      "         102\tLOAD_FAST(arg=1, lineno=1123)\n",
      "         104\tBINARY_OP(arg=11, lineno=1123)\n",
      "         108\tLOAD_FAST(arg=2, lineno=1123)\n",
      "         110\tLOAD_FAST(arg=3, lineno=1123)\n",
      "         112\tSTORE_SUBSCR(arg=None, lineno=1123)\n",
      "         116\tJUMP_BACKWARD(arg=11, lineno=1123)\n",
      ">        118\tLOAD_FAST(arg=2, lineno=1124)\n",
      "         120\tRETURN_VALUE(arg=None, lineno=1124)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | pending: deque([State(pc_initial=0 nstack_initial=0)])\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack: []\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | state.pc_initial: State(pc_initial=0 nstack_initial=0)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=0, inst=NOP(arg=None, lineno=1119)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack []\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=2, inst=RESUME(arg=0, lineno=1119)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack []\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=4, inst=LOAD_GLOBAL(arg=1, lineno=1121)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack []\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=16, inst=LOAD_ATTR(arg=1, lineno=1121)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$null$4.1', '$4load_global.0']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=26, inst=LOAD_FAST(arg=0, lineno=1121)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$null$4.1', '$16load_attr.2']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=28, inst=LOAD_GLOBAL(arg=0, lineno=1121)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$null$4.1', '$16load_attr.2', '$n26.3']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=40, inst=LOAD_ATTR(arg=2, lineno=1121)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$null$4.1', '$16load_attr.2', '$n26.3', '$28load_global.4']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=50, inst=PRECALL(arg=2, lineno=1121)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$null$4.1', '$16load_attr.2', '$n26.3', '$40load_attr.5']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=54, inst=CALL(arg=2, lineno=1121)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$null$4.1', '$16load_attr.2', '$n26.3', '$40load_attr.5']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=64, inst=STORE_FAST(arg=2, lineno=1121)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$54call.6']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=66, inst=LOAD_GLOBAL(arg=7, lineno=1122)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack []\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=78, inst=LOAD_FAST(arg=0, lineno=1122)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$null$66.8', '$66load_global.7']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=80, inst=PRECALL(arg=1, lineno=1122)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$null$66.8', '$66load_global.7', '$n78.9']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=84, inst=CALL(arg=1, lineno=1122)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$null$66.8', '$66load_global.7', '$n78.9']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=94, inst=GET_ITER(arg=None, lineno=1122)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$84call.10']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | end state. edges=[Edge(pc=96, stack=('$94get_iter.11',), blockstack=(), npush=0)]\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | pending: deque([State(pc_initial=96 nstack_initial=1)])\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack: ['$phi96.0']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | state.pc_initial: State(pc_initial=96 nstack_initial=1)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=96, inst=FOR_ITER(arg=10, lineno=1122)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$phi96.0']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | end state. edges=[Edge(pc=118, stack=(), blockstack=(), npush=0), Edge(pc=98, stack=('$phi96.0', '$96for_iter.2'), blockstack=(), npush=0)]\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | pending: deque([State(pc_initial=118 nstack_initial=0), State(pc_initial=98 nstack_initial=2)])\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack: []\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | state.pc_initial: State(pc_initial=118 nstack_initial=0)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=118, inst=LOAD_FAST(arg=2, lineno=1124)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack []\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=120, inst=RETURN_VALUE(arg=None, lineno=1124)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$out118.0']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | end state. edges=[]\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | pending: deque([State(pc_initial=98 nstack_initial=2)])\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack: ['$phi98.0', '$phi98.1']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | state.pc_initial: State(pc_initial=98 nstack_initial=2)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=98, inst=STORE_FAST(arg=3, lineno=1122)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$phi98.0', '$phi98.1']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=100, inst=LOAD_FAST(arg=3, lineno=1123)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$phi98.0']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=102, inst=LOAD_FAST(arg=1, lineno=1123)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$phi98.0', '$i100.2']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=104, inst=BINARY_OP(arg=11, lineno=1123)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$phi98.0', '$i100.2', '$d102.3']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=108, inst=LOAD_FAST(arg=2, lineno=1123)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$phi98.0', '$binop_truediv104.4']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=110, inst=LOAD_FAST(arg=3, lineno=1123)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$phi98.0', '$binop_truediv104.4', '$out108.5']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=112, inst=STORE_SUBSCR(arg=None, lineno=1123)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$phi98.0', '$binop_truediv104.4', '$out108.5', '$i110.6']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=116, inst=JUMP_BACKWARD(arg=11, lineno=1123)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$phi98.0']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | end state. edges=[Edge(pc=96, stack=('$phi98.0',), blockstack=(), npush=0)]\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | pending: deque([State(pc_initial=96 nstack_initial=1)])\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | -------------------------Prune PHIs-------------------------\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | Used_phis: defaultdict(<class 'set'>,\n",
      "            {State(pc_initial=0 nstack_initial=0): set(),\n",
      "             State(pc_initial=96 nstack_initial=1): {'$phi96.0'},\n",
      "             State(pc_initial=98 nstack_initial=2): {'$phi98.1'},\n",
      "             State(pc_initial=118 nstack_initial=0): set()})\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | defmap: {'$phi96.0': State(pc_initial=0 nstack_initial=0),\n",
      " '$phi98.1': State(pc_initial=96 nstack_initial=1)}\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | phismap: defaultdict(<class 'set'>,\n",
      "            {'$phi96.0': {('$94get_iter.11',\n",
      "                           State(pc_initial=0 nstack_initial=0)),\n",
      "                          ('$phi98.0', State(pc_initial=98 nstack_initial=2))},\n",
      "             '$phi98.0': {('$phi96.0', State(pc_initial=96 nstack_initial=1))},\n",
      "             '$phi98.1': {('$96for_iter.2',\n",
      "                           State(pc_initial=96 nstack_initial=1))}})\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | changing phismap: defaultdict(<class 'set'>,\n",
      "            {'$phi96.0': {('$94get_iter.11',\n",
      "                           State(pc_initial=0 nstack_initial=0)),\n",
      "                          ('$phi96.0', State(pc_initial=96 nstack_initial=1))},\n",
      "             '$phi98.0': {('$94get_iter.11',\n",
      "                           State(pc_initial=0 nstack_initial=0))},\n",
      "             '$phi98.1': {('$96for_iter.2',\n",
      "                           State(pc_initial=96 nstack_initial=1))}})\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | changing phismap: defaultdict(<class 'set'>,\n",
      "            {'$phi96.0': {('$94get_iter.11',\n",
      "                           State(pc_initial=0 nstack_initial=0))},\n",
      "             '$phi98.0': {('$94get_iter.11',\n",
      "                           State(pc_initial=0 nstack_initial=0))},\n",
      "             '$phi98.1': {('$96for_iter.2',\n",
      "                           State(pc_initial=96 nstack_initial=1))}})\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | changing phismap: defaultdict(<class 'set'>,\n",
      "            {'$phi96.0': {('$94get_iter.11',\n",
      "                           State(pc_initial=0 nstack_initial=0))},\n",
      "             '$phi98.0': {('$94get_iter.11',\n",
      "                           State(pc_initial=0 nstack_initial=0))},\n",
      "             '$phi98.1': {('$96for_iter.2',\n",
      "                           State(pc_initial=96 nstack_initial=1))}})\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | keep phismap: {'$phi96.0': {('$94get_iter.11', State(pc_initial=0 nstack_initial=0))},\n",
      " '$phi98.1': {('$96for_iter.2', State(pc_initial=96 nstack_initial=1))}}\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | new_out: defaultdict(<class 'dict'>,\n",
      "            {State(pc_initial=0 nstack_initial=0): {'$phi96.0': '$94get_iter.11'},\n",
      "             State(pc_initial=96 nstack_initial=1): {'$phi98.1': '$96for_iter.2'}})\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | ----------------------DONE Prune PHIs-----------------------\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | block_infos State(pc_initial=0 nstack_initial=0):\n",
      "AdaptBlockInfo(insts=((0, {}), (2, {}), (4, {'idx': 0, 'res': '$4load_global.0'}), (6, {}), (8, {}), (10, {}), (12, {}), (14, {}), (16, {'item': '$4load_global.0', 'res': '$16load_attr.2'}), (18, {}), (20, {}), (22, {}), (24, {}), (26, {'res': '$n26.3'}), (28, {'idx': 0, 'res': '$28load_global.4'}), (30, {}), (32, {}), (34, {}), (36, {}), (38, {}), (40, {'item': '$28load_global.4', 'res': '$40load_attr.5'}), (42, {}), (44, {}), (46, {}), (48, {}), (50, {}), (52, {}), (54, {'func': '$16load_attr.2', 'args': ['$n26.3', '$40load_attr.5'], 'kw_names': None, 'res': '$54call.6'}), (56, {}), (58, {}), (60, {}), (62, {}), (64, {'value': '$54call.6'}), (66, {'idx': 3, 'res': '$66load_global.7'}), (68, {}), (70, {}), (72, {}), (74, {}), (76, {}), (78, {'res': '$n78.9'}), (80, {}), (82, {}), (84, {'func': '$66load_global.7', 'args': ['$n78.9'], 'kw_names': None, 'res': '$84call.10'}), (86, {}), (88, {}), (90, {}), (92, {}), (94, {'value': '$84call.10', 'res': '$94get_iter.11'})), outgoing_phis={'$phi96.0': '$94get_iter.11'}, blockstack=(), active_try_block=None, outgoing_edgepushed={96: ('$94get_iter.11',)})\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | block_infos State(pc_initial=96 nstack_initial=1):\n",
      "AdaptBlockInfo(insts=((96, {'iterator': '$phi96.0', 'pair': '$96for_iter.1', 'indval': '$96for_iter.2', 'pred': '$96for_iter.3'}),), outgoing_phis={'$phi98.1': '$96for_iter.2'}, blockstack=(), active_try_block=None, outgoing_edgepushed={118: (), 98: ('$phi96.0', '$96for_iter.2')})\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | block_infos State(pc_initial=98 nstack_initial=2):\n",
      "AdaptBlockInfo(insts=((98, {'value': '$phi98.1'}), (100, {'res': '$i100.2'}), (102, {'res': '$d102.3'}), (104, {'op': '/', 'lhs': '$i100.2', 'rhs': '$d102.3', 'res': '$binop_truediv104.4'}), (106, {}), (108, {'res': '$out108.5'}), (110, {'res': '$i110.6'}), (112, {'target': '$out108.5', 'index': '$i110.6', 'value': '$binop_truediv104.4'}), (114, {}), (116, {})), outgoing_phis={}, blockstack=(), active_try_block=None, outgoing_edgepushed={96: ('$phi98.0',)})\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | block_infos State(pc_initial=118 nstack_initial=0):\n",
      "AdaptBlockInfo(insts=((118, {'res': '$out118.0'}), (120, {'retval': '$out118.0', 'castval': '$120return_value.1'})), outgoing_phis={}, blockstack=(), active_try_block=None, outgoing_edgepushed={})\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.interpreter | label 0:\n",
      "    n = arg(0, name=n)                       ['n']\n",
      "    d = arg(1, name=d)                       ['d']\n",
      "    $4load_global.0 = global(np: <module 'numpy' from '/Users/visser/miniforge3/envs/data/lib/python3.11/site-packages/numpy/__init__.py'>) ['$4load_global.0']\n",
      "    $16load_attr.2 = getattr(value=$4load_global.0, attr=empty) ['$16load_attr.2', '$4load_global.0']\n",
      "    $28load_global.4 = global(np: <module 'numpy' from '/Users/visser/miniforge3/envs/data/lib/python3.11/site-packages/numpy/__init__.py'>) ['$28load_global.4']\n",
      "    $40load_attr.5 = getattr(value=$28load_global.4, attr=float64) ['$28load_global.4', '$40load_attr.5']\n",
      "    out = call $16load_attr.2(n, $40load_attr.5, func=$16load_attr.2, args=[Var(n, numerics.py:1119), Var($40load_attr.5, numerics.py:1121)], kws=(), vararg=None, varkwarg=None, target=None) ['$16load_attr.2', '$40load_attr.5', 'n', 'out']\n",
      "    $66load_global.7 = global(range: <class 'range'>) ['$66load_global.7']\n",
      "    $84call.10 = call $66load_global.7(n, func=$66load_global.7, args=[Var(n, numerics.py:1119)], kws=(), vararg=None, varkwarg=None, target=None) ['$66load_global.7', '$84call.10', 'n']\n",
      "    $94get_iter.11 = getiter(value=$84call.10) ['$84call.10', '$94get_iter.11']\n",
      "    $phi96.0 = $94get_iter.11                ['$94get_iter.11', '$phi96.0']\n",
      "    jump 96                                  []\n",
      "label 96:\n",
      "    $96for_iter.1 = iternext(value=$phi96.0) ['$96for_iter.1', '$phi96.0']\n",
      "    $96for_iter.2 = pair_first(value=$96for_iter.1) ['$96for_iter.1', '$96for_iter.2']\n",
      "    $96for_iter.3 = pair_second(value=$96for_iter.1) ['$96for_iter.1', '$96for_iter.3']\n",
      "    $phi98.1 = $96for_iter.2                 ['$96for_iter.2', '$phi98.1']\n",
      "    branch $96for_iter.3, 98, 118            ['$96for_iter.3']\n",
      "label 98:\n",
      "    i = $phi98.1                             ['$phi98.1', 'i']\n",
      "    $binop_truediv104.4 = i / d              ['$binop_truediv104.4', 'd', 'i']\n",
      "    out[i] = $binop_truediv104.4             ['$binop_truediv104.4', 'i', 'out']\n",
      "    jump 96                                  []\n",
      "label 118:\n",
      "    $120return_value.1 = cast(value=out)     ['$120return_value.1', 'out']\n",
      "    return $120return_value.1                ['$120return_value.1']\n",
      "\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | ==== SSA block analysis pass on 0\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | Running <numba.core.ssa._GatherDefsHandler object at 0x143622910>\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: n = arg(0, name=n)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: d = arg(1, name=d)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: $4load_global.0 = global(np: <module 'numpy' from '/Users/visser/miniforge3/envs/data/lib/python3.11/site-packages/numpy/__init__.py'>)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: $16load_attr.2 = getattr(value=$4load_global.0, attr=empty)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: $28load_global.4 = global(np: <module 'numpy' from '/Users/visser/miniforge3/envs/data/lib/python3.11/site-packages/numpy/__init__.py'>)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: $40load_attr.5 = getattr(value=$28load_global.4, attr=float64)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: out = call $16load_attr.2(n, $40load_attr.5, func=$16load_attr.2, args=[Var(n, numerics.py:1119), Var($40load_attr.5, numerics.py:1121)], kws=(), vararg=None, varkwarg=None, target=None)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: $66load_global.7 = global(range: <class 'range'>)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: $84call.10 = call $66load_global.7(n, func=$66load_global.7, args=[Var(n, numerics.py:1119)], kws=(), vararg=None, varkwarg=None, target=None)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: $94get_iter.11 = getiter(value=$84call.10)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: $phi96.0 = $94get_iter.11\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: jump 96\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | ==== SSA block analysis pass on 96\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | Running <numba.core.ssa._GatherDefsHandler object at 0x143622910>\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: $96for_iter.1 = iternext(value=$phi96.0)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: $96for_iter.2 = pair_first(value=$96for_iter.1)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: $96for_iter.3 = pair_second(value=$96for_iter.1)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: $phi98.1 = $96for_iter.2\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: branch $96for_iter.3, 98, 118\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | ==== SSA block analysis pass on 98\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | Running <numba.core.ssa._GatherDefsHandler object at 0x143622910>\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: i = $phi98.1\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: $binop_truediv104.4 = i / d\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: out[i] = $binop_truediv104.4\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: jump 96\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | ==== SSA block analysis pass on 118\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | Running <numba.core.ssa._GatherDefsHandler object at 0x143622910>\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: $120return_value.1 = cast(value=out)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: return $120return_value.1\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | defs defaultdict(<class 'list'>,\n",
      "            {'$120return_value.1': [<numba.core.ir.Assign object at 0x14363afd0>],\n",
      "             '$16load_attr.2': [<numba.core.ir.Assign object at 0x14362fb50>],\n",
      "             '$28load_global.4': [<numba.core.ir.Assign object at 0x1436388d0>],\n",
      "             '$40load_attr.5': [<numba.core.ir.Assign object at 0x143638b90>],\n",
      "             '$4load_global.0': [<numba.core.ir.Assign object at 0x14362c490>],\n",
      "             '$66load_global.7': [<numba.core.ir.Assign object at 0x1436391d0>],\n",
      "             '$84call.10': [<numba.core.ir.Assign object at 0x1436396d0>],\n",
      "             '$94get_iter.11': [<numba.core.ir.Assign object at 0x143639990>],\n",
      "             '$96for_iter.1': [<numba.core.ir.Assign object at 0x143639c10>],\n",
      "             '$96for_iter.2': [<numba.core.ir.Assign object at 0x143639d90>],\n",
      "             '$96for_iter.3': [<numba.core.ir.Assign object at 0x143639f10>],\n",
      "             '$binop_truediv104.4': [<numba.core.ir.Assign object at 0x14363a690>],\n",
      "             '$phi96.0': [<numba.core.ir.Assign object at 0x143623590>],\n",
      "             '$phi98.1': [<numba.core.ir.Assign object at 0x143626990>],\n",
      "             'd': [<numba.core.ir.Assign object at 0x143627f90>],\n",
      "             'i': [<numba.core.ir.Assign object at 0x14363a110>],\n",
      "             'n': [<numba.core.ir.Assign object at 0x143620090>],\n",
      "             'out': [<numba.core.ir.Assign object at 0x143638e50>]})\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | SSA violators set()\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | bytecode dump:\n",
      ">          0\tNOP(arg=None, lineno=4369)\n",
      "           2\tCOPY_FREE_VARS(arg=1, lineno=4369)\n",
      "           4\tRESUME(arg=0, lineno=4369)\n",
      "           6\tLOAD_GLOBAL(arg=1, lineno=4370)\n",
      "          18\tLOAD_FAST(arg=0, lineno=4370)\n",
      "          20\tLOAD_FAST(arg=1, lineno=4370)\n",
      "          22\tLOAD_DEREF(arg=2, lineno=4370)\n",
      "          24\tPRECALL(arg=3, lineno=4370)\n",
      "          28\tCALL(arg=3, lineno=4370)\n",
      "          38\tRETURN_VALUE(arg=None, lineno=4370)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | pending: deque([State(pc_initial=0 nstack_initial=0)])\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack: []\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | state.pc_initial: State(pc_initial=0 nstack_initial=0)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=0, inst=NOP(arg=None, lineno=4369)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack []\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=2, inst=COPY_FREE_VARS(arg=1, lineno=4369)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack []\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=4, inst=RESUME(arg=0, lineno=4369)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack []\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=6, inst=LOAD_GLOBAL(arg=1, lineno=4370)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack []\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=18, inst=LOAD_FAST(arg=0, lineno=4370)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$null$6.1', '$6load_global.0']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=20, inst=LOAD_FAST(arg=1, lineno=4370)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$null$6.1', '$6load_global.0', '$shape18.2']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=22, inst=LOAD_DEREF(arg=2, lineno=4370)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$null$6.1', '$6load_global.0', '$shape18.2', '$dtype20.3']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=24, inst=PRECALL(arg=3, lineno=4370)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$null$6.1', '$6load_global.0', '$shape18.2', '$dtype20.3', '$22load_deref.4']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=28, inst=CALL(arg=3, lineno=4370)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$null$6.1', '$6load_global.0', '$shape18.2', '$dtype20.3', '$22load_deref.4']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=38, inst=RETURN_VALUE(arg=None, lineno=4370)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$28call.5']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | end state. edges=[]\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | -------------------------Prune PHIs-------------------------\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | Used_phis: defaultdict(<class 'set'>, {State(pc_initial=0 nstack_initial=0): set()})\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | defmap: {}\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | phismap: defaultdict(<class 'set'>, {})\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | changing phismap: defaultdict(<class 'set'>, {})\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | keep phismap: {}\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | new_out: defaultdict(<class 'dict'>, {})\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | ----------------------DONE Prune PHIs-----------------------\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | block_infos State(pc_initial=0 nstack_initial=0):\n",
      "AdaptBlockInfo(insts=((0, {}), (2, {}), (4, {}), (6, {'idx': 0, 'res': '$6load_global.0'}), (8, {}), (10, {}), (12, {}), (14, {}), (16, {}), (18, {'res': '$shape18.2'}), (20, {'res': '$dtype20.3'}), (22, {'res': '$22load_deref.4'}), (24, {}), (26, {}), (28, {'func': '$6load_global.0', 'args': ['$shape18.2', '$dtype20.3', '$22load_deref.4'], 'kw_names': None, 'res': '$28call.5'}), (30, {}), (32, {}), (34, {}), (36, {}), (38, {'retval': '$28call.5', 'castval': '$38return_value.6'})), outgoing_phis={}, blockstack=(), active_try_block=None, outgoing_edgepushed={})\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.interpreter | label 0:\n",
      "    shape = arg(0, name=shape)               ['shape']\n",
      "    dtype = arg(1, name=dtype)               ['dtype']\n",
      "    $6load_global.0 = global(numpy_empty_nd: <intrinsic numpy_empty_nd>) ['$6load_global.0']\n",
      "    $22load_deref.4 = freevar(retty: array(float64, 1d, C)) ['$22load_deref.4']\n",
      "    $28call.5 = call $6load_global.0(shape, dtype, $22load_deref.4, func=$6load_global.0, args=[Var(shape, arrayobj.py:4369), Var(dtype, arrayobj.py:4369), Var($22load_deref.4, arrayobj.py:4370)], kws=(), vararg=None, varkwarg=None, target=None) ['$22load_deref.4', '$28call.5', '$6load_global.0', 'dtype', 'shape']\n",
      "    $38return_value.6 = cast(value=$28call.5) ['$28call.5', '$38return_value.6']\n",
      "    return $38return_value.6                 ['$38return_value.6']\n",
      "\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | ==== SSA block analysis pass on 0\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | Running <numba.core.ssa._GatherDefsHandler object at 0x143688150>\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: shape = arg(0, name=shape)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: dtype = arg(1, name=dtype)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: $6load_global.0 = global(numpy_empty_nd: <intrinsic numpy_empty_nd>)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: $22load_deref.4 = freevar(retty: array(float64, 1d, C))\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: $28call.5 = call $6load_global.0(shape, dtype, $22load_deref.4, func=$6load_global.0, args=[Var(shape, arrayobj.py:4369), Var(dtype, arrayobj.py:4369), Var($22load_deref.4, arrayobj.py:4370)], kws=(), vararg=None, varkwarg=None, target=None)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: $38return_value.6 = cast(value=$28call.5)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: return $38return_value.6\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | defs defaultdict(<class 'list'>,\n",
      "            {'$22load_deref.4': [<numba.core.ir.Assign object at 0x1436265d0>],\n",
      "             '$28call.5': [<numba.core.ir.Assign object at 0x1436d0810>],\n",
      "             '$38return_value.6': [<numba.core.ir.Assign object at 0x1436d0a50>],\n",
      "             '$6load_global.0': [<numba.core.ir.Assign object at 0x143678b90>],\n",
      "             'dtype': [<numba.core.ir.Assign object at 0x1436b9450>],\n",
      "             'shape': [<numba.core.ir.Assign object at 0x14368a690>]})\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | SSA violators set()\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | bytecode dump:\n",
      ">          0\tNOP(arg=None, lineno=4245)\n",
      "           2\tRESUME(arg=0, lineno=4245)\n",
      "           4\tLOAD_FAST(arg=0, lineno=4248)\n",
      "           6\tLOAD_METHOD(arg=0, lineno=4248)\n",
      "          28\tLOAD_FAST(arg=1, lineno=4248)\n",
      "          30\tLOAD_FAST(arg=2, lineno=4248)\n",
      "          32\tPRECALL(arg=2, lineno=4248)\n",
      "          36\tCALL(arg=2, lineno=4248)\n",
      "          46\tRETURN_VALUE(arg=None, lineno=4248)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | pending: deque([State(pc_initial=0 nstack_initial=0)])\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack: []\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | state.pc_initial: State(pc_initial=0 nstack_initial=0)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=0, inst=NOP(arg=None, lineno=4245)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack []\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=2, inst=RESUME(arg=0, lineno=4245)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack []\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=4, inst=LOAD_FAST(arg=0, lineno=4248)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack []\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=6, inst=LOAD_METHOD(arg=0, lineno=4248)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$arrtype4.0']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=28, inst=LOAD_FAST(arg=1, lineno=4248)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$null$6.1', '$6load_method.2']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=30, inst=LOAD_FAST(arg=2, lineno=4248)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$null$6.1', '$6load_method.2', '$size28.3']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=32, inst=PRECALL(arg=2, lineno=4248)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$null$6.1', '$6load_method.2', '$size28.3', '$align30.4']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=36, inst=CALL(arg=2, lineno=4248)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$null$6.1', '$6load_method.2', '$size28.3', '$align30.4']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=46, inst=RETURN_VALUE(arg=None, lineno=4248)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$36call.5']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | end state. edges=[]\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | -------------------------Prune PHIs-------------------------\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | Used_phis: defaultdict(<class 'set'>, {State(pc_initial=0 nstack_initial=0): set()})\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | defmap: {}\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | phismap: defaultdict(<class 'set'>, {})\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | changing phismap: defaultdict(<class 'set'>, {})\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | keep phismap: {}\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | new_out: defaultdict(<class 'dict'>, {})\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | ----------------------DONE Prune PHIs-----------------------\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | block_infos State(pc_initial=0 nstack_initial=0):\n",
      "AdaptBlockInfo(insts=((0, {}), (2, {}), (4, {'res': '$arrtype4.0'}), (6, {'item': '$arrtype4.0', 'res': '$6load_method.2'}), (8, {}), (10, {}), (12, {}), (14, {}), (16, {}), (18, {}), (20, {}), (22, {}), (24, {}), (26, {}), (28, {'res': '$size28.3'}), (30, {'res': '$align30.4'}), (32, {}), (34, {}), (36, {'func': '$6load_method.2', 'args': ['$size28.3', '$align30.4'], 'kw_names': None, 'res': '$36call.5'}), (38, {}), (40, {}), (42, {}), (44, {}), (46, {'retval': '$36call.5', 'castval': '$46return_value.6'})), outgoing_phis={}, blockstack=(), active_try_block=None, outgoing_edgepushed={})\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.interpreter | label 0:\n",
      "    arrtype = arg(0, name=arrtype)           ['arrtype']\n",
      "    size = arg(1, name=size)                 ['size']\n",
      "    align = arg(2, name=align)               ['align']\n",
      "    $6load_method.2 = getattr(value=arrtype, attr=_allocate) ['$6load_method.2', 'arrtype']\n",
      "    $36call.5 = call $6load_method.2(size, align, func=$6load_method.2, args=[Var(size, arrayobj.py:4245), Var(align, arrayobj.py:4245)], kws=(), vararg=None, varkwarg=None, target=None) ['$36call.5', '$6load_method.2', 'align', 'size']\n",
      "    $46return_value.6 = cast(value=$36call.5) ['$36call.5', '$46return_value.6']\n",
      "    return $46return_value.6                 ['$46return_value.6']\n",
      "\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | ==== SSA block analysis pass on 0\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | Running <numba.core.ssa._GatherDefsHandler object at 0x143786fd0>\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: arrtype = arg(0, name=arrtype)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: size = arg(1, name=size)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: align = arg(2, name=align)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: $6load_method.2 = getattr(value=arrtype, attr=_allocate)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: $36call.5 = call $6load_method.2(size, align, func=$6load_method.2, args=[Var(size, arrayobj.py:4245), Var(align, arrayobj.py:4245)], kws=(), vararg=None, varkwarg=None, target=None)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: $46return_value.6 = cast(value=$36call.5)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: return $46return_value.6\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | defs defaultdict(<class 'list'>,\n",
      "            {'$36call.5': [<numba.core.ir.Assign object at 0x143716b10>],\n",
      "             '$46return_value.6': [<numba.core.ir.Assign object at 0x143716e50>],\n",
      "             '$6load_method.2': [<numba.core.ir.Assign object at 0x143716590>],\n",
      "             'align': [<numba.core.ir.Assign object at 0x143715fd0>],\n",
      "             'arrtype': [<numba.core.ir.Assign object at 0x1436fa150>],\n",
      "             'size': [<numba.core.ir.Assign object at 0x143714950>]})\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | SSA violators set()\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | bytecode dump:\n",
      ">          0\tNOP(arg=None, lineno=4240)\n",
      "           2\tRESUME(arg=0, lineno=4240)\n",
      "           4\tLOAD_GLOBAL(arg=1, lineno=4241)\n",
      "          16\tLOAD_FAST(arg=1, lineno=4241)\n",
      "          18\tLOAD_FAST(arg=2, lineno=4241)\n",
      "          20\tPRECALL(arg=2, lineno=4241)\n",
      "          24\tCALL(arg=2, lineno=4241)\n",
      "          34\tRETURN_VALUE(arg=None, lineno=4241)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | pending: deque([State(pc_initial=0 nstack_initial=0)])\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack: []\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | state.pc_initial: State(pc_initial=0 nstack_initial=0)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=0, inst=NOP(arg=None, lineno=4240)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack []\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=2, inst=RESUME(arg=0, lineno=4240)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack []\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=4, inst=LOAD_GLOBAL(arg=1, lineno=4241)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack []\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=16, inst=LOAD_FAST(arg=1, lineno=4241)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$null$4.1', '$4load_global.0']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=18, inst=LOAD_FAST(arg=2, lineno=4241)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$null$4.1', '$4load_global.0', '$allocsize16.2']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=20, inst=PRECALL(arg=2, lineno=4241)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$null$4.1', '$4load_global.0', '$allocsize16.2', '$align18.3']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=24, inst=CALL(arg=2, lineno=4241)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$null$4.1', '$4load_global.0', '$allocsize16.2', '$align18.3']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | dispatch pc=34, inst=RETURN_VALUE(arg=None, lineno=4241)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | stack ['$24call.4']\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | end state. edges=[]\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | -------------------------Prune PHIs-------------------------\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | Used_phis: defaultdict(<class 'set'>, {State(pc_initial=0 nstack_initial=0): set()})\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | defmap: {}\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | phismap: defaultdict(<class 'set'>, {})\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | changing phismap: defaultdict(<class 'set'>, {})\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | keep phismap: {}\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | new_out: defaultdict(<class 'dict'>, {})\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | ----------------------DONE Prune PHIs-----------------------\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.byteflow | block_infos State(pc_initial=0 nstack_initial=0):\n",
      "AdaptBlockInfo(insts=((0, {}), (2, {}), (4, {'idx': 0, 'res': '$4load_global.0'}), (6, {}), (8, {}), (10, {}), (12, {}), (14, {}), (16, {'res': '$allocsize16.2'}), (18, {'res': '$align18.3'}), (20, {}), (22, {}), (24, {'func': '$4load_global.0', 'args': ['$allocsize16.2', '$align18.3'], 'kw_names': None, 'res': '$24call.4'}), (26, {}), (28, {}), (30, {}), (32, {}), (34, {'retval': '$24call.4', 'castval': '$34return_value.5'})), outgoing_phis={}, blockstack=(), active_try_block=None, outgoing_edgepushed={})\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.interpreter | label 0:\n",
      "    cls = arg(0, name=cls)                   ['cls']\n",
      "    allocsize = arg(1, name=allocsize)       ['allocsize']\n",
      "    align = arg(2, name=align)               ['align']\n",
      "    $4load_global.0 = global(intrin_alloc: <intrinsic intrin_alloc>) ['$4load_global.0']\n",
      "    $24call.4 = call $4load_global.0(allocsize, align, func=$4load_global.0, args=[Var(allocsize, arrayobj.py:4240), Var(align, arrayobj.py:4240)], kws=(), vararg=None, varkwarg=None, target=None) ['$24call.4', '$4load_global.0', 'align', 'allocsize']\n",
      "    $34return_value.5 = cast(value=$24call.4) ['$24call.4', '$34return_value.5']\n",
      "    return $34return_value.5                 ['$34return_value.5']\n",
      "\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | ==== SSA block analysis pass on 0\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | Running <numba.core.ssa._GatherDefsHandler object at 0x1437b3010>\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: cls = arg(0, name=cls)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: allocsize = arg(1, name=allocsize)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: align = arg(2, name=align)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: $4load_global.0 = global(intrin_alloc: <intrinsic intrin_alloc>)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: $24call.4 = call $4load_global.0(allocsize, align, func=$4load_global.0, args=[Var(allocsize, arrayobj.py:4240), Var(align, arrayobj.py:4240)], kws=(), vararg=None, varkwarg=None, target=None)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: $34return_value.5 = cast(value=$24call.4)\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | on stmt: return $34return_value.5\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | defs defaultdict(<class 'list'>,\n",
      "            {'$24call.4': [<numba.core.ir.Assign object at 0x1437b5890>],\n",
      "             '$34return_value.5': [<numba.core.ir.Assign object at 0x1437b5b50>],\n",
      "             '$4load_global.0': [<numba.core.ir.Assign object at 0x1437b5290>],\n",
      "             'align': [<numba.core.ir.Assign object at 0x1437b5050>],\n",
      "             'allocsize': [<numba.core.ir.Assign object at 0x1437b40d0>],\n",
      "             'cls': [<numba.core.ir.Assign object at 0x1437b2110>]})\n",
      "15:35:53 | \u001b[36mDEBUG   \u001b[0m| numba.core.ssa | SSA violators set()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<details open>\n",
       "    <summary><strong>General</strong></summary>\n",
       "    <table class=\"table table-hover table-striped table-sm table-responsive small\">\n",
       "        <tr>\n",
       "            <th>Measurement date</th>\n",
       "            \n",
       "            <td>Unknown</td>\n",
       "            \n",
       "        </tr>\n",
       "        <tr>\n",
       "            <th>Experimenter</th>\n",
       "            \n",
       "            <td>Unknown</td>\n",
       "            \n",
       "        </tr>\n",
       "        <tr>\n",
       "            <th>Participant</th>\n",
       "            \n",
       "            <td>Unknown</td>\n",
       "            \n",
       "        </tr>\n",
       "    </table>\n",
       "    </details>\n",
       "    <details open>\n",
       "        <summary><strong>Channels</strong></summary>\n",
       "        <table class=\"table table-hover table-striped table-sm table-responsive small\">\n",
       "            <tr>\n",
       "                <th>Digitized points</th>\n",
       "                \n",
       "                <td>Not available</td>\n",
       "                \n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Good channels</th>\n",
       "                <td>8 EEG</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Bad channels</th>\n",
       "                <td>None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>EOG channels</th>\n",
       "                <td>Not available</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>ECG channels</th>\n",
       "                <td>Not available</td>\n",
       "            </tr>\n",
       "        </table>\n",
       "        </details>\n",
       "        <details open>\n",
       "            <summary><strong>Data</strong></summary>\n",
       "            <table class=\"table table-hover table-striped table-sm table-responsive small\">\n",
       "                \n",
       "                <tr>\n",
       "                    <th>Sampling frequency</th>\n",
       "                    <td>500.00 Hz</td>\n",
       "                </tr>\n",
       "                \n",
       "                \n",
       "                <tr>\n",
       "                    <th>Highpass</th>\n",
       "                    <td>0.00 Hz</td>\n",
       "                </tr>\n",
       "                \n",
       "                \n",
       "                <tr>\n",
       "                    <th>Lowpass</th>\n",
       "                    <td>250.00 Hz</td>\n",
       "                </tr>\n",
       "                \n",
       "                \n",
       "                \n",
       "                \n",
       "                <tr>\n",
       "                    <th>Duration</th>\n",
       "                    <td>00:04:40 (HH:MM:SS)</td>\n",
       "                </tr>\n",
       "                \n",
       "            </table>\n",
       "            </details>"
      ],
      "text/plain": [
       "<RawArray | 8 x 139999 (280.0 s), ~8.6 MB, data loaded>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert to numpy array and transpose to get channels x timeseries\n",
    "eeg_data = eeg_raw.select(pl.col(\"^EEG_.*$\")).to_numpy().T\n",
    "\n",
    "# Create the info structure needed by MNE\n",
    "ch_names = [\n",
    "    \"EEG 001\",\n",
    "    \"EEG 002\",\n",
    "    \"EEG 003\",\n",
    "    \"EEG 004\",\n",
    "    \"EEG 005\",\n",
    "    \"EEG 006\",\n",
    "    \"EEG 007\",\n",
    "    \"EEG 008\",\n",
    "]\n",
    "ch_types = [\"eeg\"] * 8\n",
    "info = mne.create_info(ch_names=ch_names, sfreq=sampling_rate, ch_types=ch_types)\n",
    "eeg_mne = mne.io.RawArray(eeg_data, info)\n",
    "eeg_mne"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questions\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# - Artifacture removal for 8-channels? ICA not possible?\n",
    "# - Power analysis? Which method? Window size? Only alpha? Or all bands?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15:36:06 | \u001b[93mWARNING \u001b[0m| transform_data | Only one trial found, applying function to the whole DataFrame.\n",
      "15:36:06 | \u001b[1m\u001b[91m\u001b[4mCRITICAL\u001b[0m| transform_data | Applying function scale_min_max to each trial. We still have to find out if order is maintained.\n",
      "15:36:06 | \u001b[93mWARNING \u001b[0m| transform_data | Only one trial found, applying function to the whole DataFrame.\n",
      "15:36:06 | \u001b[1m\u001b[91m\u001b[4mCRITICAL\u001b[0m| transform_data | Applying function interpolate to each trial. We still have to find out if order is maintained.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (148_365, 12)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Timestamp</th><th>EEG_RAW_Ch1</th><th>EEG_RAW_Ch2</th><th>EEG_RAW_Ch3</th><th>EEG_RAW_Ch4</th><th>EEG_RAW_Ch5</th><th>EEG_RAW_Ch6</th><th>EEG_RAW_Ch7</th><th>EEG_RAW_Ch8</th><th>Trial</th><th>Temperature</th><th>Rating</th></tr><tr><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>56840.9667</td><td>0.802223</td><td>0.301762</td><td>0.179487</td><td>0.807584</td><td>0.605033</td><td>0.424066</td><td>0.657671</td><td>0.479359</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>56844.8285</td><td>0.788133</td><td>0.320553</td><td>0.180603</td><td>0.802768</td><td>0.608438</td><td>0.422272</td><td>0.663095</td><td>0.482243</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>56846.4889</td><td>0.782445</td><td>0.334044</td><td>0.181647</td><td>0.80857</td><td>0.608586</td><td>0.429853</td><td>0.674762</td><td>0.492341</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>336843.0288</td><td>0.281306</td><td>0.441109</td><td>0.914089</td><td>0.169819</td><td>0.39112</td><td>0.675714</td><td>0.294858</td><td>0.513249</td><td>0.0</td><td>0.85543</td><td>0.533824</td></tr><tr><td>336845.1234</td><td>0.26309</td><td>0.413367</td><td>0.904612</td><td>0.165494</td><td>0.387121</td><td>0.679507</td><td>0.282925</td><td>0.517037</td><td>0.0</td><td>0.85543</td><td>0.534412</td></tr><tr><td>336849.2292</td><td>0.208029</td><td>0.336947</td><td>0.868589</td><td>0.116835</td><td>0.323907</td><td>0.530434</td><td>0.208328</td><td>0.423291</td><td>0.0</td><td>0.85543</td><td>0.535</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (148_365, 12)\n",
       "┌────────────┬────────────┬────────────┬────────────┬───┬───────────┬───────┬───────────┬──────────┐\n",
       "│ Timestamp  ┆ EEG_RAW_Ch ┆ EEG_RAW_Ch ┆ EEG_RAW_Ch ┆ … ┆ EEG_RAW_C ┆ Trial ┆ Temperatu ┆ Rating   │\n",
       "│ ---        ┆ 1          ┆ 2          ┆ 3          ┆   ┆ h8        ┆ ---   ┆ re        ┆ ---      │\n",
       "│ f64        ┆ ---        ┆ ---        ┆ ---        ┆   ┆ ---       ┆ f64   ┆ ---       ┆ f64      │\n",
       "│            ┆ f64        ┆ f64        ┆ f64        ┆   ┆ f64       ┆       ┆ f64       ┆          │\n",
       "╞════════════╪════════════╪════════════╪════════════╪═══╪═══════════╪═══════╪═══════════╪══════════╡\n",
       "│ 56840.9667 ┆ 0.802223   ┆ 0.301762   ┆ 0.179487   ┆ … ┆ 0.479359  ┆ 0.0   ┆ 0.0       ┆ 0.0      │\n",
       "│ 56844.8285 ┆ 0.788133   ┆ 0.320553   ┆ 0.180603   ┆ … ┆ 0.482243  ┆ 0.0   ┆ 0.0       ┆ 0.0      │\n",
       "│ 56846.4889 ┆ 0.782445   ┆ 0.334044   ┆ 0.181647   ┆ … ┆ 0.492341  ┆ 0.0   ┆ 0.0       ┆ 0.0      │\n",
       "│ …          ┆ …          ┆ …          ┆ …          ┆ … ┆ …         ┆ …     ┆ …         ┆ …        │\n",
       "│ 336840.968 ┆ 0.25299    ┆ 0.412359   ┆ 0.894371   ┆ … ┆ 0.449254  ┆ 0.0   ┆ 0.85543   ┆ 0.533235 │\n",
       "│ 3          ┆            ┆            ┆            ┆   ┆           ┆       ┆           ┆          │\n",
       "│ 336843.028 ┆ 0.281306   ┆ 0.441109   ┆ 0.914089   ┆ … ┆ 0.513249  ┆ 0.0   ┆ 0.85543   ┆ 0.533824 │\n",
       "│ 8          ┆            ┆            ┆            ┆   ┆           ┆       ┆           ┆          │\n",
       "│ 336845.123 ┆ 0.26309    ┆ 0.413367   ┆ 0.904612   ┆ … ┆ 0.517037  ┆ 0.0   ┆ 0.85543   ┆ 0.534412 │\n",
       "│ 4          ┆            ┆            ┆            ┆   ┆           ┆       ┆           ┆          │\n",
       "│ 336849.229 ┆ 0.208029   ┆ 0.336947   ┆ 0.868589   ┆ … ┆ 0.423291  ┆ 0.0   ┆ 0.85543   ┆ 0.535    │\n",
       "│ 2          ┆            ┆            ┆            ┆   ┆           ┆       ┆           ┆          │\n",
       "└────────────┴────────────┴────────────┴────────────┴───┴───────────┴───────┴───────────┴──────────┘"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = merge_dfs(eeg_raw, heat)\n",
    "merged = scale_min_max(merged)\n",
    "merged = interpolate(merged)\n",
    "merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_trial_matplotlib(merged, trial=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n  var py_version = '3.3.3'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  var reloading = false;\n  var Bokeh = root.Bokeh;\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks;\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      run_callbacks();\n      return null;\n    }\n    if (!reloading) {\n      console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    }\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n    window._bokeh_on_load = on_load\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    var skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {'plotly': 'https://cdn.plot.ly/plotly-2.25.2.min', 'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n      require([\"plotly\"], function(Plotly) {\n\twindow.Plotly = Plotly\n\ton_load()\n      })\n      require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n      })\n      require([\"jspanel-modal\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-tooltip\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-hint\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-layout\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-dock\"], function() {\n\ton_load()\n      })\n      require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n      })\n      require([\"notyf\"], function() {\n\ton_load()\n      })\n      root._bokeh_is_loading = css_urls.length + 10;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    var existing_stylesheets = []\n    var links = document.getElementsByTagName('link')\n    for (var i = 0; i < links.length; i++) {\n      var link = links[i]\n      if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n      }\n    }\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }    if (((window['Plotly'] !== undefined) && (!(window['Plotly'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.3.7/dist/bundled/plotlyplot/plotly-2.25.2.min.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.3.7/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.3.7/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.3.7/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.3.7/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.3.7/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.3.7/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.3.7/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n      for (var i = 0; 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       "  var docs_json = {\"308273d1-dcca-4d13-b5ec-9fab12988525\":{\"version\":\"3.3.3\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"p1004\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"p1005\",\"attributes\":{\"plot_id\":\"p1004\",\"comm_id\":\"c007e4a9defc4ac996eab234f1b1bb89\",\"client_comm_id\":\"67b528a180394dff95dc9e45ae1696e7\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
       "  var render_items = [{\"docid\":\"308273d1-dcca-4d13-b5ec-9fab12988525\",\"roots\":{\"p1004\":\"ef1c94e3-6be8-49e3-b347-0e5efea8fecf\"},\"root_ids\":[\"p1004\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined && ( root['Plotly'] !== undefined))\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
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       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:param.main: backend option not found for line plot with plotly; similar options include: []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15:36:14 | \u001b[93mWARNING \u001b[0m| param.main | backend option not found for line plot with plotly; similar options include: []\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:bokeh.server.server:Starting Bokeh server version 3.3.3 (running on Tornado 6.3.3)\n",
      "INFO:bokeh.server.tornado:User authentication hooks NOT provided (default user enabled)\n",
      "DEBUG:bokeh.server.tornado:These host origins can connect to the websocket: ['localhost:56626']\n",
      "DEBUG:bokeh.server.tornado:Patterns are:\n",
      "DEBUG:bokeh.server.tornado:  [('/favicon.ico',\n",
      "DEBUG:bokeh.server.tornado:    <class 'bokeh.server.views.ico_handler.IcoHandler'>,\n",
      "DEBUG:bokeh.server.tornado:    {'app': <bokeh.server.tornado.BokehTornado object at 0x14360ea50>}),\n",
      "DEBUG:bokeh.server.tornado:   ('/?',\n",
      "DEBUG:bokeh.server.tornado:    <class 'panel.io.server.DocHandler'>,\n",
      "DEBUG:bokeh.server.tornado:    {'application_context': <bokeh.server.contexts.ApplicationContext object at 0x14b8e5fd0>,\n",
      "DEBUG:bokeh.server.tornado:     'bokeh_websocket_path': '/ws'}),\n",
      "DEBUG:bokeh.server.tornado:   ('/ws',\n",
      "DEBUG:bokeh.server.tornado:    <class 'bokeh.server.views.ws.WSHandler'>,\n",
      "DEBUG:bokeh.server.tornado:    {'application_context': <bokeh.server.contexts.ApplicationContext object at 0x14b8e5fd0>,\n",
      "DEBUG:bokeh.server.tornado:     'bokeh_websocket_path': '/ws',\n",
      "DEBUG:bokeh.server.tornado:     'compression_level': None,\n",
      "DEBUG:bokeh.server.tornado:     'mem_level': None}),\n",
      "DEBUG:bokeh.server.tornado:   ('/metadata',\n",
      "DEBUG:bokeh.server.tornado:    <class 'bokeh.server.views.metadata_handler.MetadataHandler'>,\n",
      "DEBUG:bokeh.server.tornado:    {'application_context': <bokeh.server.contexts.ApplicationContext object at 0x14b8e5fd0>,\n",
      "DEBUG:bokeh.server.tornado:     'bokeh_websocket_path': '/ws'}),\n",
      "DEBUG:bokeh.server.tornado:   ('/autoload.js',\n",
      "DEBUG:bokeh.server.tornado:    <class 'panel.io.server.AutoloadJsHandler'>,\n",
      "DEBUG:bokeh.server.tornado:    {'application_context': <bokeh.server.contexts.ApplicationContext object at 0x14b8e5fd0>,\n",
      "DEBUG:bokeh.server.tornado:     'bokeh_websocket_path': '/ws'}),\n",
      "DEBUG:bokeh.server.tornado:   ('/__patchedroot/static/(.*)',\n",
      "DEBUG:bokeh.server.tornado:    <class 'bokeh.server.views.static_handler.StaticHandler'>,\n",
      "DEBUG:bokeh.server.tornado:    {}),\n",
      "DEBUG:bokeh.server.tornado:   ('/components/(.*)',\n",
      "DEBUG:bokeh.server.tornado:    <class 'panel.io.server.ComponentResourceHandler'>,\n",
      "DEBUG:bokeh.server.tornado:    {}),\n",
      "DEBUG:bokeh.server.tornado:   ('/?',\n",
      "DEBUG:bokeh.server.tornado:    <class 'panel.io.server.RootHandler'>,\n",
      "DEBUG:bokeh.server.tornado:    {'applications': {'/': <bokeh.server.contexts.ApplicationContext object at 0x14b8e5fd0>},\n",
      "DEBUG:bokeh.server.tornado:     'index': '/Users/visser/miniforge3/envs/data/lib/python3.11/site-packages/panel/io/../_templates/index.html',\n",
      "DEBUG:bokeh.server.tornado:     'prefix': '',\n",
      "DEBUG:bokeh.server.tornado:     'use_redirect': True}),\n",
      "DEBUG:bokeh.server.tornado:   ('/static/extensions/(.*)',\n",
      "DEBUG:bokeh.server.tornado:    <class 'bokeh.server.views.multi_root_static_handler.MultiRootStaticHandler'>,\n",
      "DEBUG:bokeh.server.tornado:    {'root': {'panel': PosixPath('/Users/visser/miniforge3/envs/data/lib/python3.11/site-packages/panel/dist')}}),\n",
      "DEBUG:bokeh.server.tornado:   ('/static/(.*)',\n",
      "DEBUG:bokeh.server.tornado:    <class 'bokeh.server.views.static_handler.StaticHandler'>)]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Launching server at http://localhost:56626\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tornado.access:200 GET / (127.0.0.1) 315.98ms\n",
      "INFO:tornado.access:200 GET /static/extensions/panel/bundled/jquery/jquery.slim.min.js (127.0.0.1) 4.44ms\n",
      "INFO:tornado.access:200 GET /static/js/bokeh-gl.min.js?v=bf37f0b457d54fefb6ca8423c37db6ae69479153907d223a22f57d090b957998e75abda056bf5b0916a24f99930fa6df3b242a1a3a0986b549fbc966c1e04416 (127.0.0.1) 12.10ms\n",
      "INFO:tornado.access:200 GET /static/js/bokeh-widgets.min.js?v=3c2dbaf226dc96c10bf3dfbcde30557363d2c16ec86bf2a10fb615e53d3971cbcf801e5051aa500292ec49f54812deae2aec9aaad0d97331534c89fe18ede89a (127.0.0.1) 12.37ms\n",
      "INFO:tornado.access:200 GET /static/js/bokeh-tables.min.js?v=7849f2320ea741465a49857765873105e961ae71f15b481c5c529e76627d3706d004fe429b0472d4ba9e19fac7cf9be1874ba144e996beb84d89fe89958293f4 (127.0.0.1) 12.56ms\n",
      "INFO:tornado.access:200 GET /static/extensions/panel/panel.min.js?v=0faff9f97292449eee32b81df88f812bca038d443c5a2254802df6108463f0fe (127.0.0.1) 15.85ms\n",
      "INFO:tornado.access:200 GET /static/js/bokeh.min.js?v=f43c49e86dc38c1a13b9f41aad15fb57c3b2f70844817e5559b32d9e0a177c319416281f7bac18181198884ceb3998420b37b2b0199e0d0dc6485e34fc0a28dc (127.0.0.1) 17.98ms\n",
      "INFO:tornado.access:200 GET /static/extensions/panel/bundled/plotlyplot/plotly-2.25.2.min.js (127.0.0.1) 54.34ms\n",
      "DEBUG:bokeh.server.views.ws:Subprotocol header received\n",
      "INFO:bokeh.server.views.ws:WebSocket connection opened\n",
      "INFO:tornado.access:101 GET /ws (127.0.0.1) 3.17ms\n",
      "DEBUG:bokeh.server.views.ws:Receiver created for Protocol()\n",
      "DEBUG:bokeh.server.views.ws:ProtocolHandler created for Protocol()\n",
      "INFO:bokeh.server.views.ws:ServerConnection created\n",
      "INFO:tornado.access:200 GET /static/extensions/panel/images/favicon.ico (127.0.0.1) 2.15ms\n",
      "INFO:tornado.access:200 GET /static/extensions/panel/images/apple-touch-icon.png (127.0.0.1) 1.04ms\n",
      "DEBUG:bokeh.server.session:Sending pull-doc-reply from session 'icS0kCwbhMzBEJ17B78oHDnZvLO6TMTvZ7ClPAdDRpew'\n",
      "INFO:tornado.access:200 GET /static/extensions/panel/css/loading.css?v=1.3.7 (127.0.0.1) 2.99ms\n",
      "INFO:tornado.access:200 GET /static/extensions/panel/css/listpanel.css?v=1.3.7 (127.0.0.1) 1.76ms\n",
      "INFO:tornado.access:200 GET /static/extensions/panel/bundled/theme/default.css?v=1.3.7 (127.0.0.1) 2.58ms\n",
      "INFO:tornado.access:200 GET /static/extensions/panel/bundled/theme/native.css?v=1.3.7 (127.0.0.1) 3.21ms\n",
      "INFO:tornado.access:200 GET /static/extensions/panel/css/plotly.css?v=1.3.7 (127.0.0.1) 1.57ms\n",
      "INFO:tornado.access:200 GET /static/extensions/panel/css/select.css?v=1.3.7 (127.0.0.1) 2.08ms\n",
      "INFO:tornado.access:200 GET /static/extensions/panel/css/widgetbox.css?v=1.3.7 (127.0.0.1) 1.82ms\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 1 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 0 unused\n",
      "INFO:bokeh.server.views.ws:WebSocket connection closed: code=1001, reason=None\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 0 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 1 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 0 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 1 sessions with 1 unused\n",
      "DEBUG:bokeh.server.contexts:Scheduling 1 sessions to discard\n",
      "DEBUG:bokeh.server.contexts:Discarding session 'icS0kCwbhMzBEJ17B78oHDnZvLO6TMTvZ7ClPAdDRpew' last in use 24428.725875020027 milliseconds ago\n",
      "DEBUG:bokeh.document.modules:Deleting 0 modules for document <bokeh.document.document.Document object at 0x14b88fe90>\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 0 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 0 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 0 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 0 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 0 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 0 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 0 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 0 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 0 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 0 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 0 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 0 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 0 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 0 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 0 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 0 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 0 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 0 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 0 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 0 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 0 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 0 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 0 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 0 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 0 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 0 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 0 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 0 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 0 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 0 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 0 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 0 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 0 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 0 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 0 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 0 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 0 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 0 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 0 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 0 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 0 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 0 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 0 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 0 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 0 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 0 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 0 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 0 sessions with 0 unused\n",
      "DEBUG:bokeh.server.tornado:[pid 71693] 0 clients connected\n",
      "DEBUG:bokeh.server.tornado:[pid 71693]   / has 0 sessions with 0 unused\n"
     ]
    }
   ],
   "source": [
    "plot_data_panel(merged)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17:40:15 |\u001b[36m DEBUG   \u001b[0m| h5py._conv | Creating converter from 7 to 5\n",
      "17:40:15 |\u001b[36m DEBUG   \u001b[0m| h5py._conv | Creating converter from 5 to 7\n",
      "17:40:15 |\u001b[36m DEBUG   \u001b[0m| h5py._conv | Creating converter from 7 to 5\n",
      "17:40:15 |\u001b[36m DEBUG   \u001b[0m| h5py._conv | Creating converter from 5 to 7\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "module 'wonambi' has no attribute 'create_data'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[9], line 5\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mwonambi\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mwn\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# generate data\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mwn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_data\u001b[49m(n_chan\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, signal\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msine\u001b[39m\u001b[38;5;124m'\u001b[39m, amplitude\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m      7\u001b[0m traces \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m      8\u001b[0m     go\u001b[38;5;241m.\u001b[39mScatter(\n\u001b[1;32m      9\u001b[0m         x\u001b[38;5;241m=\u001b[39mdata\u001b[38;5;241m.\u001b[39mtime[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m     10\u001b[0m         y\u001b[38;5;241m=\u001b[39mdata(trial\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, chan\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mchan00\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[1;32m     11\u001b[0m     ]\n\u001b[1;32m     12\u001b[0m layout \u001b[38;5;241m=\u001b[39m go\u001b[38;5;241m.\u001b[39mLayout(\n\u001b[1;32m     13\u001b[0m     xaxis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mdict\u001b[39m(\n\u001b[1;32m     14\u001b[0m         title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTime (s)\u001b[39m\u001b[38;5;124m'\u001b[39m),\n\u001b[1;32m     15\u001b[0m     yaxis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mdict\u001b[39m(\n\u001b[1;32m     16\u001b[0m         title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAmplitude (V)\u001b[39m\u001b[38;5;124m'\u001b[39m))\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'wonambi' has no attribute 'create_data'"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tornado.access:200 GET / (127.0.0.1) 296.61ms\n",
      "INFO:tornado.access:200 GET /static/extensions/panel/bundled/jquery/jquery.slim.min.js (127.0.0.1) 1.07ms\n",
      "INFO:tornado.access:200 GET /static/js/bokeh.min.js?v=f43c49e86dc38c1a13b9f41aad15fb57c3b2f70844817e5559b32d9e0a177c319416281f7bac18181198884ceb3998420b37b2b0199e0d0dc6485e34fc0a28dc (127.0.0.1) 5.41ms\n",
      "INFO:tornado.access:200 GET /static/extensions/panel/bundled/plotlyplot/plotly-2.18.0.min.js (127.0.0.1) 66.91ms\n",
      "INFO:tornado.access:200 GET /static/js/bokeh-gl.min.js?v=bf37f0b457d54fefb6ca8423c37db6ae69479153907d223a22f57d090b957998e75abda056bf5b0916a24f99930fa6df3b242a1a3a0986b549fbc966c1e04416 (127.0.0.1) 0.64ms\n",
      "INFO:tornado.access:200 GET /static/js/bokeh-widgets.min.js?v=3c2dbaf226dc96c10bf3dfbcde30557363d2c16ec86bf2a10fb615e53d3971cbcf801e5051aa500292ec49f54812deae2aec9aaad0d97331534c89fe18ede89a (127.0.0.1) 1.24ms\n",
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      "INFO:tornado.access:200 GET /static/extensions/panel/panel.min.js?v=813b231f281e55d9af5ea2364998b4d88d5d8c59158214c140f2d0f812d36d5d (127.0.0.1) 1.89ms\n",
      "INFO:tornado.access:101 GET /ws (127.0.0.1) 0.64ms\n",
      "INFO:tornado.access:200 GET /static/extensions/panel/images/favicon.ico (127.0.0.1) 1.67ms\n",
      "INFO:tornado.access:200 GET /static/extensions/panel/images/apple-touch-icon.png (127.0.0.1) 5.53ms\n",
      "INFO:tornado.access:200 GET /static/extensions/panel/bundled/theme/native.css?v=1.3.6 (127.0.0.1) 2.62ms\n",
      "INFO:tornado.access:200 GET /static/extensions/panel/css/plotly.css?v=1.3.6 (127.0.0.1) 3.32ms\n",
      "INFO:tornado.access:200 GET /static/extensions/panel/css/widgetbox.css?v=1.3.6 (127.0.0.1) 3.92ms\n",
      "INFO:tornado.access:200 GET /static/extensions/panel/css/loading.css?v=1.3.6 (127.0.0.1) 4.37ms\n",
      "INFO:tornado.access:200 GET /static/extensions/panel/css/listpanel.css?v=1.3.6 (127.0.0.1) 4.84ms\n",
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     ]
    }
   ],
   "source": [
    "import wonambi as wn\n",
    "\n",
    "# generate data\n",
    "data = wn.create_data(n_chan=2, signal=\"sine\", amplitude=1)\n",
    "\n",
    "traces = [go.Scatter(x=data.time[0], y=data(trial=0, chan=\"chan00\"))]\n",
    "layout = go.Layout(xaxis=dict(title=\"Time (s)\"), yaxis=dict(title=\"Amplitude (V)\"))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "learning",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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